CN106485605A - Clean energy resource electricity step price forward purchasing platform and control method - Google Patents
Clean energy resource electricity step price forward purchasing platform and control method Download PDFInfo
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Abstract
The invention discloses a kind of clean energy resource electricity step price forward purchasing platform and control method.As Fig. 1, it is mainly by clean energy resource electricity ultra-short term prognoses system 1（With 4 hours for a cycle）, clean energy resource electricity short-term forecast system 2（With 24 hours for a cycle）, sale of electricity control system 3, timesharing divides the pre- sale system of valency 4, and user's electric load prognoses system 5 is constituted.Clean energy resource capacitance and period are sent to sale of electricity control system by clean energy resource electricity ultra-short term prognoses system and clean energy resource electricity short-term forecast system in real time, whether sale of electricity control system carries out comprehensive analysis price in low-valley interval according to clean energy resource electricity ratio and user's block of purchase electricity in electrical network, and divides in the pre- sale system of valency in timesharing and carry out presell.Then by existing power purchase system, the electricity buied is issued in the power purchase card of user.The present invention can be floated by electricity price and dissolve clean energy resource electric power, realize electrical equipment energy-conservation, low consumption, economical operation, reach effect that is more intelligent, effectively distributing clean energy resource.
Description
Technical field
The present invention relates to clean energy resource utilizes field, relate generally to sale of electricity regulator control system and control method, timesharing divides valency
Pre- sale system and control algolithm.
Background technology
With wind-powered electricity generation, photoelectricity, renewable energy power fast development, clean energy resource electricity will account for greatly in electrical network
Share, the randomness of clean energy resource electricity makes peak load regulation network load pressure increase, and cost increases.The peak regulation of electrical network also can enable cleaning
Source generation is unsalable, abandons electrical phenomena.Therefore, the prediction according to clean energy resource generated energy, the ladder electricity of the different periods of clean energy resource
The inexorable trend of valency necessarily China's electric Power Reform.A kind of clean energy resource step price forward purchasing platform participating in peak load regulation network, will
Become a kind of principal mode of following peak load regulation network load.
Content of the invention
Present invention is primarily aimed at the pressure that the randomness solving clean energy resource brings to electrical network, optimize electrical network distribution.
By set up sale of electricity regulation platform reach to electrical network clean energy resource electricity capacity and user to clean energy resource electricity demand carry out whole
Close analysis, determine sale of electricity time period and sale of electricity price, improve citizen to the producing level of clean energy resource and enthusiasm, in certain journey
The utilization of clean energy resource is improved on degree, mitigates the pressure of electrical network.The system and control method mainly suitably use in micro-capacitance sensor.
The purpose of the present invention to be solved by technical scheme below：
Appeal clean energy resource electricity ultra-short term prognoses system -1 is provided with wind energy ultra-short term prediction reception system 2-1, solar energy ultra-short term
Prediction accepts receipts system 2-2, biomass energy ultra-short term predicts reception system 2-3, ultra-short term generated energy prediction in nuclear energy power generation field connects
Receipts system 2-4, statistical model set up module 2-5 and statistical result output module 2-6.Wherein prediction reception system can be according to micro-
Electrical network clean energy resource construction is flexibly changed.Statistical model sets up the number that module 2-5 is come to collection with weighting chronological average method
According to being modeled, model, is integrated with four hours for a short cycle to the clean energy resource electricity of prediction for 24 hours for a long period
Calculate, four hours short cycle starting points are prediction starting point.Output result is to sale of electricity control system -3.
Appeal clean energy resource electricity short-term forecast system -2 is provided with the similar knot of same clean energy resource electricity ultra-short term prognoses system -1
Structure pattern, except for the difference that, clean energy resource electricity short-term forecast system is provided with wind energy short-term forecast reception system 3-1, solar energy short-term
Prediction reception system 3-2, biomass energy short-term forecast system receive system 3-3, nuclear energy power generation field short-term electricity generation amount prediction reception system
3-4.Prediction reception system flexibly can be changed according to micro-capacitance sensor clean energy resource construction.
User's electric load prognoses system -5 of appeal is provided with data receiver timesharing module 4-1, model building module 4-2, number
It is predicted that module 4-3, data transmit module 4-4.The user's history that data receiver timesharing module 4-1 sends to grid company
Electricity consumption data with four seasons spring, summer, autumn and winter piecemeal, then in each season module with 24h and 4h for cycle piecemeal；Model building module 4-
Method enters 0 modeling to cycle module under 2 pairs of each season modules in temporal sequence；Data prediction module 4-3 sets up mould according to model
The model that block is set up is predicted to micro-capacitance sensor future 4h and 24h power load.Carry out data sharing with grid company, collect micro-
User's history electricity consumption data in electrical network, and four seasons spring, summer, autumn and winter forecast model is set up respectively according to time series method, in corresponding season
Section, with 24h and 4h for period forecasting future micro-capacitance sensor power load, sends and predicts the outcome to sale of electricity control system -3.
Timesharing divides the platform that the pre- sale system of valency -4 is user's power purchase, and timesharing divides the pre- sale system -4 of valency to control system according to sale of electricity
The data display sale of electricity price that system -3 is sent and time period are selected for user, and electric surpluses are sent to sale of electricity control system
System -3.Timesharing divide the pre- sale system of valency -4 be divided into data receiver storage management system 5-1, clean energy resource electricity presell management platform 5-2,
Clean energy resource electrical network page presell platform 5-3, clean energy resource mobile phone A PP presell platform 5-4, clean energy resource wechat public number presell are put down
Platform 5-5.
The sale of electricity control system -3 of appeal is the control core of whole platform, is divided into data reception module 1-1, electricity price price
Adjusting module 1-2.Data reception module 1-1 receives from ultra-short term clean energy resource prognoses system -1 and the prediction of short-term clean energy resource
The data of system -2, and the user power utilization amount that predicts of user's electric load prognoses system -5, timesharing divide the pre- sale system of valency -4 to obtain
Electric surpluses.And this two groups of data are carried out with classification integration, in electricity price pricing adjustments module 1-2, foundation judges sort module
Algorithm flow determines clean energy resource electricity to be sold and price, is sent to timesharing and divides valency presell platform -4 to be sold.
Judge sort module algorithm flow：First, when paid-for time is for ultra-short term prediction period, i.e. range prediction starting point
In 4 hours, passage in time is gradually reduced by electricity price, is divided into 4 electricity prices（Each hour is an electricity price, and last length is predicted
In starting point 1 hour, electricity price is 0）.2nd, when paid-for time is for the short-term forecast period, i.e. range prediction starting point 4 ~ 24 hours
When interior, in clean energy resource electricity ratio in electrical network and low-valley interval valuation, that is, when clean energy resource electricity accounts for electrical network ratio P/ δ >=B
(22%), calculated by clean energy resource electricity price, when clean energy resource electricity accounts for electrical network ratio P/ δ >=B (22%) and belongs to and underestimates electricity price
Period M:22：00~6：When 00, comprehensive clean energy resource electricity price and low ebb electricity price calculate electricity price, when clean energy resource electricity accounts for electrical network ratio
P/ δ≤B (22%) and belong to and underestimate rate period M:22：00~6：When 00, calculate by low ebb electricity price.Clean energy resource electricity accounts for electrical network ratio
Example B, electricity consumption market price, underestimate electricity price, clean energy resource electricity price and underestimate rate period M can be adjusted accordingly according to policy at that time defeated
Enter.
Advantages of the present invention：
1st, sale of electricity control system can carry out real-time adjustment according to the content of regenerative resource electricity and period in electrical network to electricity price, and
Divide in timesharing and sold on valency platform, the method improves the transparency to electricity price price, so that electricity price is more rationalized.
2nd, improve the saving consciousness of the public, due to carrying out timesharing points price sales to electricity price in the system, user will more incline
To in using more cheap clean energy resource electricity, improve the saving consciousness of resident to a certain extent.
3rd, the system makes citizen be more likely to using clear to the real-time adjustment scheme of electrical network electricity price and the foundation of sale of electricity platform
Clean energy electricity, reaches the purpose of clean energy resource electricity of dissolving.
4th, clean energy resource electricity, due to its randomness, uncertainty, can cause very big pressure to electrical network if direct grid-connected
Power.And clean energy resource generating step price is purchased platform in advance and can be increased the usage amount of the electricity of clean energy resource in electrical network, eliminate electrical network because
The power system collapse causing for the grid-connected in a large number of clean energy resource electricity.
Brief description：
Fig. 1：Clean energy resource generating step price purchases platform structure figure in advance
Fig. 2：Clean energy resource electricity ultra-short term prognoses system -1 structure chart
Fig. 3：Clean energy resource electricity short-term forecast system -2 structure chart
Fig. 4：User's electric load prognoses system -5 structure chart
Fig. 5：Timesharing divides the pre- sale system of valency -4 structure chart
Fig. 6：Judge sort module algorithm flow block diagram
Specific embodiment：
Clean energy resource electricity ultra-short term prognoses system -1 receive from wind power plant, solar power plant, biomass power plant,
The generated energy prediction data in 4 hours futures that nuclear plant is sent using GPRS wireless network.Wind energy ultra-short term is predicted
Reception system 2-1, the prediction of solar energy ultra-short term accept receipts system 2-2, biomass energy ultra-short term prediction reception system 2-3, nuclear energy
The data is activation of reception is set up module 2-5 to statistical model by generating field ultra-short term generated energy prediction reception system 2-4, counts mould
Type is set up module 2-5 and data is distributed in time carry out arrangement integration according to weighting chronological average method, with every four hours is
One short cycle was modeled calculating, and statistical result output 2-6 sends result to sale of electricity control system -3 by cable data.Defeated
Go out generated energy P, time period T (n).
Clean energy resource electricity short-term forecast system -2 receives and is derived from wind power plant, solar power plant, biomass power generation
The generated energy prediction data in 24 hours futures that factory, nuclear plant are sent using GPRS wireless network.Wind energy short-term is pre-
Survey reception system 3-1, solar energy short-term forecast accepts receipts system 3-2, biomass energy short-term forecast reception system 3-3, nuclear energy are sent out
The data is activation of reception is set up module 3-5 to statistical model by electric field short-term electricity generation amount prediction reception system 3-4, and statistical model is built
Formwork erection block 3-5 is distributed in time carrying out arrangement integration according to weighting chronological average method to data, short for one with every four hours
Cycle is modeled calculating, and statistical result output 3-6 sends result to sale of electricity control system -3 by cable data.Output is sent out
Electricity W, time period T (j).
User's electric load prognoses system -5 of appeal is provided with data receiver timesharing module 4-1, model building module 4-2, number
It is predicted that module 4-3, data transmit module 4-4.The user's history that data receiver timesharing module 4-1 sends to grid company
Electricity consumption data with four seasons spring, summer, autumn and winter piecemeal, then in each season module with 24h and 4h for cycle piecemeal；Model building module 4-
Each cycle module under 2 pairs of each season modules is pressed one kind and is based on pattern recognition non parametric regression（PRAPR）Model is built
Mould；Data prediction module 4-3 is entered to micro-capacitance sensor future 4h and 24h power load according to the model that model building module 4-2 sets up
Row prediction.Transmission predicts the outcome to sale of electricity control system -3.The system need to carry out data sharing with grid company, collects micro-capacitance sensor
Middle user's history electricity consumption data.
Timesharing divides the platform that the pre- sale system of valency -4 is user's power purchase, and timesharing divides the pre- sale system of valency to be divided into data receiver memotron
Reason system 5-1, clean energy resource electricity presell management platform 5-2, clean energy resource electrical network page presell platform 5-3, clean energy resource mobile phone A PP
Presell platform 5-4, clean energy resource wechat public number presell platform 5-5.Sale of electricity is controlled system by data receiver storage management system 5-1
Capacitance, electricity price and corresponding period that system -3 is sent store and arrange and be sent to clean energy resource electricity presell management platform 5-2, cleaning
Energy electricity presell management platform 5-2 sends sale of electricity period, electricity price and surpluses respectively to clean energy resource electrical network page presell platform 5-
3rd, clean energy resource mobile phone A PP presell platform 5-4, clean energy resource wechat public number presell platform 5-5.Clean energy resource three kinds of presells of electricity
With certain arrangement mode, platform shows that institute's sale of electricity period, electricity price and surpluses carry out sale of electricity.Clean energy resource three kinds of presells of electricity are put down
The purchase situation of user is also sent to clean energy resource electricity presell management platform 5-2, clean energy resource electricity presell management platform 5-2 by platform
Situation is bought according to user and capacitance calculates electric surpluses and is sent respectively to clean energy resource three kinds of presell platforms of electricity and sale of electricity
Control system -3.
Sale of electricity control system -3 is the control core of whole platform, is divided into data reception module 1-1, electricity price pricing adjustments mould
Block 1-2.Data reception module 1-1 receives and is derived from ultra-short term clean energy resource prognoses system -1 and short-term clean energy resource prognoses system -2
Data, and the user power utilization amount that predicts of user's electric load prognoses system -5, timesharing divide the electricity that the pre- sale system of valency -4 obtains to remain
Surplus.And this two groups of data are carried out with classification integration, in electricity price pricing adjustments module 1-2, foundation judges sort module algorithm stream
Journey determines clean energy resource electricity to be sold and price, is sent to timesharing and divides valency presell platform -4 to be sold.
Claims (4)
1. clean energy resource electricity step price forward purchasing platform（As Fig. 1）It is characterised in that：It includes clean energy resource electricity ultra-short term prediction
System -1（With 4 hours for a cycle）, clean energy resource electricity short-term forecast system -2（With 24 hours for a cycle）, sale of electricity control system
System -3, timesharing divides valency pre- sale system -4, user's electric load prognoses system -5；Clean energy resource electricity ultra-short term prognoses system -1 and cleaning
Energy electricity short-term forecast system -2 receives, by GPRS wireless network, prediction electricity and the period that clean energy resource power plant sends, and passes through
Modeling processes and issues sale of electricity control system -3, and sale of electricity control system -3 receive user electric load prognoses system -5, timesharing divide valency pre-
Sale system -4 and the data of two prognoses systems, through judging that sort module algorithm flow carries out electricity price price and divide valency in timesharing
Pre- sale system -4 carries out presell, then by existing power purchase system, the electricity that user buys is issued in the power purchase card of user.
2. clean energy resource electricity step price forward purchasing platform courses method it is characterised in that：He mainly uses and judges that sort module is calculated
Method flow process（As Fig. 6）Carry out electricity price classification to control, one, when paid-for time is for ultra-short term prediction period, i.e. range prediction initiates
In point 4 hours, passage in time is gradually reduced by electricity price, is divided into 4 electricity prices（Each hour is an electricity price, and last length is pre-
Surveying electricity price in starting point 1 hour is 0）；2nd, when paid-for time is for the short-term forecast period, that is, range prediction starting point 4 ~ 24 is little
When interior when, in electrical network clean energy resource electricity ratio and low-valley interval valuation, that is, when clean energy resource electricity accounts for electrical network ratio P/ δ >=B
(22%), calculated by clean energy resource electricity price, when clean energy resource electricity accounts for electrical network ratio P/ δ >=B (22%) and belongs to and underestimates electricity price
Period M:22：00~6：When 00, comprehensive clean energy resource electricity price and low ebb electricity price calculate electricity price, when clean energy resource electricity accounts for electrical network ratio
P/ δ≤B (22%) and belong to and underestimate rate period M:22：00~6：When 00, calculate by low ebb electricity price；Clean energy resource electricity accounts for electrical network ratio
Example B, electricity consumption market price, underestimate electricity price, clean energy resource electricity price and underestimate rate period M can be adjusted accordingly according to policy at that time defeated
Enter.
3. the user's electric load prognoses system told according to claim 1（As Fig. 4）It is characterised in that：It is provided with data receiver to divide
When module 4-1, model building module 4-2, data prediction module 4-3, data transmit module 4-4；Data receiver timesharing mould
The user's history electricity consumption data that block 4-1 sends to grid company with four seasons spring, summer, autumn and winter piecemeal, then in each season module with 24h
It is cycle piecemeal with 4h；To the cycle module under each season module, method enters 0 modeling to model building module 4-2 in temporal sequence；
Data prediction module 4-3 is predicted to micro-capacitance sensor future 4h and 24h power load according to the model that model building module is set up；
Carry out data sharing with grid company, collect user's history electricity consumption data in micro-capacitance sensor, and set up respectively according to time series method
Four seasons spring, summer, autumn and winter forecast model, in corresponding season with 24h and 4h for period forecasting future micro-capacitance sensor power load, sends prediction
Result is to sale of electricity control system -3.
4. divide valency pre- sale system according to the timesharing that claim 1 is told（As Fig. 5）It is characterised in that：It is divided into data receiver to store up
Deposit management system 5-1, clean energy resource electricity presell management platform 5-2, clean energy resource electrical network page presell platform 5-3, clean energy resource handss
Machine APP presell platform 5-4, clean energy resource wechat public number presell platform 5-5；Timesharing divides the pre- sale system -4 of valency according to sale of electricity control
The data display sale of electricity price that system -3 is sent and time period are selected for user, and electric surpluses are sent to sale of electricity control
System -3.
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| CN112016977A (en) * | 2020-09-04 | 2020-12-01 | 国网山东省电力公司莱芜供电公司 | Method and system for calculating and acquiring electricity consumption information with stepped electricity price optimization model and electricity quantity data server |
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